diff --git a/tests/saving/test_unsloth_save.py b/tests/saving/test_unsloth_save.py
index 22dd38c8d1..fb3593c4b0 100644
--- a/tests/saving/test_unsloth_save.py
+++ b/tests/saving/test_unsloth_save.py
@@ -26,6 +26,7 @@ model_to_test = [
 save_file_sizes = {}
 save_file_sizes["merged_16bit"] = {}
 save_file_sizes["merged_4bit"] = {}
+save_file_sizes["torchao"] = {}
 
 tokenizer_files = [
     "tokenizer_config.json",
@@ -168,7 +169,7 @@ def test_save_merged_4bit(model, tokenizer, temp_save_dir: str):
         load_in_4bit=True,
     )
 
-@pytest.mark.skipif(importlib.util.find_spec("torchao") is None)
+@pytest.mark.skipif(importlib.util.find_spec("torchao") is None, reason="require torchao to be installed")
 def test_save_torchao(model, tokenizer, temp_save_dir: str):
     save_path = os.path.join(temp_save_dir, "unsloth_torchao", model.config._name_or_path.replace("/", "_"))
 
@@ -178,6 +179,7 @@ def test_save_torchao(model, tokenizer, temp_save_dir: str):
         save_path,
         tokenizer=tokenizer,
         torchao_config=torchao_config,
+        push_to_hub=False,
     )
 
     # Check model files
@@ -193,13 +195,13 @@ def test_save_torchao(model, tokenizer, temp_save_dir: str):
 
     # Store the size of the model files
     total_size = sum(os.path.getsize(os.path.join(save_path, f)) for f in weight_files)
-    save_file_sizes["merged_4bit"][model.config._name_or_path] = total_size
+    save_file_sizes["torchao"][model.config._name_or_path] = total_size
 
-    print(f"Total size of merged_4bit files: {total_size} bytes")
+    # merged_16bit tests are not running yet, so we can't test this for now
+    # TODO: enable this after merged_16bit is fixed
+    # assert total_size < save_file_sizes["merged_16bit"][model.config._name_or_path], "torchao files are larger than merged 16bit files."
 
-    assert total_size < save_file_sizes["merged_16bit"][model.config._name_or_path], "Merged 4bit files are larger than merged 16bit files."
-
-    # Check config to see if it is 4bit
+    # Check config to see if it is quantized with torchao
     config_path = os.path.join(save_path, "config.json")
     with open(config_path, "r") as f:
         config = json.load(f)
@@ -207,9 +209,11 @@ def test_save_torchao(model, tokenizer, temp_save_dir: str):
     assert "quantization_config" in config, "Quantization config not found in the model config."
 
     # Test loading the model from the saved path
+    # can't set `load_in_4bit` to True because the model is torchao quantized
+    # can't quantize again with bitsandbytes
     loaded_model, loaded_tokenizer = FastModel.from_pretrained(
         save_path,
         max_seq_length=128,
         dtype=None,
-        load_in_4bit=True,
+        load_in_4bit=False,
     )
diff --git a/unsloth/save.py b/unsloth/save.py
index 62f79c4f5c..cf3670b127 100644
--- a/unsloth/save.py
+++ b/unsloth/save.py
@@ -2116,6 +2116,7 @@ def unsloth_push_to_hub_gguf(
     pass
 
     if fix_bos_token:
+
         logger.warning(
             "Unsloth: ##### The current model auto adds a BOS token.\n"\
             "Unsloth: ##### We removed it in GGUF's chat template for you."
@@ -2511,7 +2512,7 @@ def unsloth_save_pretrained_torchao(
     Args
       `save_directory`: local folder path or huggingface hub ID when `push_to_hub` is set to True, e.g. `my_model`
       `torchao_config` (TorchAOBaseConfig): configuration for torchao quantization, full list: https://docs.pytorch.org/ao/main/api_ref_quantization.html#inference-apis-for-quantize
-      `push_to_hub` (bool): whether to push the checkpoint to huggingface hub or not
+      `push_to_hub` (bool): whether to push the checkpoint to huggingface hub or save locally
     """
     # first merge the lora weights
     arguments = dict(locals())
@@ -2550,7 +2551,9 @@ def unsloth_save_pretrained_torchao(
         # torchao does not support safe_serialization right now
         model.push_to_hub(save_directory, safe_serialization = False, token = token)
         tokenizer.push_to_hub(save_directory, token = token)
-        pass
+    else:
+        model.save_pretrained(save_directory, safe_serialization=False)
+        tokenizer.save_pretrained(save_directory)
     pass
     for _ in range(3):
         gc.collect()